simpleml.pipelines.external_pipelines
¶
Wrapper class for a pickleable pipeline of a series of transformers
Module Contents¶
Classes¶
Use default dictionary behavior but add wrapper methods for |
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Use default sklearn behavior but add wrapper methods for |
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class
simpleml.pipelines.external_pipelines.
DefaultPipeline
[source]¶ Bases:
collections.OrderedDict
Use default dictionary behavior but add wrapper methods for extended functionality
Initialize self. See help(type(self)) for accurate signature.
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fit_transform
(self, X, y=None, **kwargs)[source]¶ Iterate through each transformation step and apply fit and transform
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get_feature_names
(self, feature_names)[source]¶ Iterate through each transformer and return list of resulting features starts with empty list by default but can pass in dataset as starting point to guide transformations
- Parameters
feature_names – list of initial feature names before transformations
- Type
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get_params
(self, params_only=None, **kwargs)[source]¶ Iterate through transformers and return parameters
- Parameters
params_only – Unused parameter to align signature with Sklearn version
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class
simpleml.pipelines.external_pipelines.
SklearnPipeline
(steps, *, memory=None, verbose=False)[source]¶ Bases:
sklearn.pipeline.Pipeline
Use default sklearn behavior but add wrapper methods for extended functionality
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add_transformer
(self, name, transformer, index=None)[source]¶ Setter method for new transformer step
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get_feature_names
(self, feature_names)[source]¶ Iterate through each transformer and return list of resulting features starts with empty list by default but can pass in dataset as starting point to guide transformations
- Parameters
feature_names – list of initial feature names before transformations
- Type
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